Learned color taste associations in a repeated brief exposure paradigm

Individuals can learn to associate tastes with odors through repeated exposure. We adapted this paradigm, testing whether individuals can learn to associate tastes with colors, and whether these learned color-taste associations generalize to unconditioned, but qualitatively similar stimuli. Experiment 1 tested if individuals could learn color-taste associations for prototypical tastes, while Experiment 2 tested if individuals could learn to discriminate specific bitter compounds using color-taste associations. Conditioned stimuli in Experiment 1 consisted of solutions representing four different prototypical taste qualities. Conditioned stimuli in Experiment 2 consisted of three different bitter stimuli and a sucrose control. Both experiments consisted of six laboratory visits. On day 1, participants rated intensities of conditioned and unconditioned stimuli. On days 2–5 participants completed triangle tests, where a color was systematically paired with high/low intensity combinations of the conditioned stimuli. On day 6, participants matched colorless solutions containing the conditioned and unconditioned stimuli to one of four colors in a forced choice task. In Experiment 1, group performance was significantly above chance (25%) when matching the conditioned taste stimulus to the previously paired color. Also, learning generalized to unconditioned stimuli with a similar taste quality. In Experiment 2, group performance versus chance (25%) indicated participants learned associated sweetness with the previously paired color, both for sucrose (conditioned stimulus) and aspartame (unconditioned stimulus). However, matching performance did not exceed chance for the three different bitterants. These data suggest that particular color-taste associations can be learned with repeated exposure, even when the exposure period is relatively brief.     

A new fast associative classification algorithm for detecting phishing websites

Associative classification (AC) is a new, effective supervised learning approach that aims to predict unseen instances. AC effectively integrates association rule mining and classification, and produces more accurate results than other traditional data mining classification algorithms. In this paper, we propose a new AC algorithm called the Fast Associative Classification Algorithm (FACA). We investigate our proposed algorithm against four well-known AC algorithms (CBA, CMAR, MCAR, and ECAR) on real-world phishing datasets. The bases of the investigation in our experiments are classification accuracy and the F1 evaluation measures. The results indicate that FACA is very successful with regard to the F1 evaluation measure compared with the other four well-known algorithms (CBA, CMAR, MCAR, and ECAR). The FACA also outperformed the other four AC algorithms with regard to the accuracy evaluation measure.     

衡敛系统的性质及其推导

开放系统能够构建衡敛的状态,成为衡敛系统,根据收敛级数的性质进行系统结构解析,得出衡敛系统的三个基本性质,可对衡敛系统结构进行分解和组合。通过对衡敛系统性质的进一步推导,又得出了衡敛系统的守恒律和相对律,由此形成了研究复杂性科学的新理论和方法,能够实现从定性到定量、从微观到宏观进行系统科学研究,这种运用衡敛系统理论的分析方法,是一条进行复杂性科学研究的新路径。     

Adaptive particle swarm optimization for CNN associative memories design

In this paper particle swarm optimization is used to implement a synthesis procedure for cellular neural networks autoassociative memories. The use of this optimization technique allows a global search for computing the model parameters that identify designed memories, providing a synthesis procedure that takes into account the robustness of the solution. In particular, the design parameters can be modified during the convergence in order to guarantee minimum recall performances of the network in terms of robustness to noise overlapped to input patterns. Numerical results confirm the good performances of the designed networks when patterns are affected by different kinds of noise.     

Novel associative classifier based on dynamic adaptive PSO: Application to determining candidates for thoracic surgery

Association rule mining is a data mining technique for discovering useful and novel patterns or relationships from databases. These rules are simple to infer and intuitive and can be easily used for classification in any domain that requires explanation for and investigation into how the classification works. Examples of such areas are medicine, agriculture, education, etc. For such a system to find wide adoptability, it should give output that is correct and comprehensible. The amount of data has been growing very fast and so has the search space of these problems. So we need to change traditional methods. This paper discusses a rule mining classifier called DA-AC (dynamic adaptive-associative classifier) which is based on a Dynamic Particle Swarm Optimizer. Due to its seeding method, exemplar selection, adaptive parameters, dynamic reconstruction of regions and velocity update, it avoids premature convergence and provides a better value in every dimension. Quality evaluation is done both for individual rules as well as entire rulesets. Experiments were conducted over fifteen benchmark datasets to evaluate performance of proposed algorithm in comparison with six other state-of-the-art non associative classifiers and eight associative classifiers. Results demonstrate competitive performance of proposed DA-AC while considering predictive accuracy and number of mined patterns as parameters. The method was then applied to predict life expectancy of post operative thoracic surgery patients.     

基于相关规则的不平衡数据的关联分类

许多研究表明关联分类具有较高的分类准确率,然而,大多数关联分类基于"支持度-置信度"框架,在不平衡数据集中,置信度和支持度都偏向产生多数类的规则,因此,少数类的实例容易被错误分类。针对上述问题,提出了一种基于相关规则的不平衡数据的关联分类算法。该算法挖掘频繁且互关联的项集,在以该项集为前件的分类规则中选取提升度最大的规则。规则按结合了提升度、置信度和补类支持度(CCS)的规则强度进行排序。实验表明,该算法取得了较高的平均分类准确率且在分类少数类的实例时具有更高的准确率。     

关联集合在协同过滤推荐算法中的应用研究

为了提高电子商务推荐系统的精度,提出了基于关联集合的协同过滤推荐算法,该算法通过频繁项集生成算法生成一系列频繁项集,然后通过合并处理过滤掉与用户关联很小的一些噪音项目,从而使协同过滤算法更加有效。该算法在推荐精度上比传统的方法优越。     

Lower bounds for the bilinear complexity of associative algebras

Let R(A) denote the bilinear complexity (also called rank) of a finite dimensional associative algebra A.?We prove that if the decomposition of into simple algebras contains only noncommutative factors, that is, the division algebra is noncommutative or . In particular, -matrix multiplication requires at least essential bilinear multiplications. We also derive lower bounds of the form essential bilinear multiplications. We also derive lower bounds of the form for the algebra of upper triangular -matrices and the algebra of truncated bivariate polynomials in the indeterminates X,Y over some field k.?A class of algebras that has received wide attention in this context con-sists of those algebras A for which the Alder—Strassen Bound is sharp, i.e., R(A) = 2dim A—t is the number of maximal twosided ideals in A. These algebras are called algebras of minimal rank. We determine all semisimple algebras of minimal rank over arbitrary fields and all algebras of minimal rank over algebraically closed fields. Received: January 12, 2000.